Inference Optimization Intern – Performance Modeling

Responsibilities

• Develop analytical performance models for GPU kernels and inference workloads
• Build and validate a simulator to estimate theoretical hardware performance limits
• Identify performance bottlenecks in compute, memory, communication, and scheduling
• Analyze GPU execution using NVIDIA Nsight Systems and Nsight Compute
• Investigate PTX and SASS code generation to understand low-level execution behavior
• Collaborate with researchers to optimize inference kernels for transformer-based models
• Design profiling methodologies for Hopper and Blackwell architectures

Requirements

• Currently pursuing a degree in Computer Science, Computer Engineering, Electrical Engineering, AI, HPC, or a related discipline
• Proficiency in C++, CUDA, and Python
• Knowledge of NVIDIA GPU architecture and memory hierarchy
• Experience with CUDA programming and GPU kernel development
• Familiarity with performance profiling tools like Nsight Systems or Nsight Compute
• Understanding of PTX or SASS

Preferred Qualifications

• Experience optimizing CUDA kernels for throughput and latency
• Understanding of roofline analysis and hardware utilization metrics
• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Strong analytical and debugging abilities

About the Company

The Institute of Foundation Models is dedicated to advancing the science and engineering of large-scale AI systems. Our researchers and engineers develop cutting-edge foundation models while pushing the limits of high-performance computing and efficient AI inference.